{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.core.api import DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "from pyreadstat import pyreadstat\n",
    "import mytools\n",
    "import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df,metadata =pyreadstat.read_sav(R\"data/企业形象调查原始数据.sav\",apply_value_formats=True)\n",
    "df1,metadata = mytools.read_spss(R\"data/企业形象调查原始数据.sav\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>都市生活</th>\n",
       "      <th>...</th>\n",
       "      <th>麦当劳就职意愿</th>\n",
       "      <th>麦当劳股票</th>\n",
       "      <th>麦当劳综合评价</th>\n",
       "      <th>必胜客企业认知度</th>\n",
       "      <th>必胜客广告接触度</th>\n",
       "      <th>必胜客就职意愿</th>\n",
       "      <th>必胜客股票</th>\n",
       "      <th>必胜客综合评价</th>\n",
       "      <th>认知维度</th>\n",
       "      <th>情感维度</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [问卷编号, 调查员, 性别, 年龄, 职业, 伙食费, 常去的快餐店, 欢乐聚餐, 快乐童年, 都市生活, 洋溢青春, 其他感受, 德克士类型感知, 服务态度, 健康安全, 快速便捷, 价格昂贵, 口感不好, 品类太少, 其他评价, 快速好吃, 服务好, 种类多, 价格便宜, 其他吸引因素, 服务态度需改进, 送餐速度, 就餐环境, 产品价格, 促销活动, 产品种类, 其他改进因素, 德克士企业认知度, 德克士广告接触度, 德克士就职意愿, 德克士股票, 德克士综合评价, 肯德基企业认知度, 肯德基广告接触度, 肯德基就职意愿, 肯德基股票, 肯德基综合评价, 麦当劳企业认知度, 麦当劳广告接触度, 麦当劳就职意愿, 麦当劳股票, 麦当劳综合评价, 必胜客企业认知度, 必胜客广告接触度, 必胜客就职意愿, 必胜客股票, 必胜客综合评价, 认知维度, 情感维度]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 54 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isnull().T.any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.dropna(thresh=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2.drop_duplicates(subset=['问卷编号'],keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>情感维度</th>\n",
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       "    </tr>\n",
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      ],
      "text/plain": [
       "                 0\n",
       "问卷编号       float64\n",
       "调查员         object\n",
       "性别        category\n",
       "年龄         float64\n",
       "职业          object\n",
       "伙食费       category\n",
       "常去的快餐店    category\n",
       "欢乐聚餐       float64\n",
       "快乐童年       float64\n",
       "都市生活       float64\n",
       "洋溢青春       float64\n",
       "其他感受       float64\n",
       "德克士类型感知   category\n",
       "服务态度      category\n",
       "健康安全       float64\n",
       "快速便捷       float64\n",
       "价格昂贵       float64\n",
       "口感不好       float64\n",
       "品类太少       float64\n",
       "其他评价       float64\n",
       "快速好吃       float64\n",
       "服务好        float64\n",
       "种类多        float64\n",
       "价格便宜       float64\n",
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       "产品价格       float64\n",
       "促销活动       float64\n",
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       "德克士企业认知度  category\n",
       "德克士广告接触度  category\n",
       "德克士就职意愿   category\n",
       "德克士股票     category\n",
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       "肯德基企业认知度  category\n",
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       "肯德基就职意愿   category\n",
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       "必胜客广告接触度  category\n",
       "必胜客就职意愿   category\n",
       "必胜客股票     category\n",
       "必胜客综合评价   category\n",
       "认知维度       float64\n",
       "情感维度       float64"
      ]
     },
     "execution_count": 7,
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   "source": [
    "df3.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['性别', '伙食费', '常去的快餐店', '德克士类型感知', '服务态度', '德克士企业认知度', '德克士广告接触度',\n",
       "       '德克士就职意愿', '德克士股票', '德克士综合评价', '肯德基企业认知度', '肯德基广告接触度', '肯德基就职意愿',\n",
       "       '肯德基股票', '肯德基综合评价', '麦当劳企业认知度', '麦当劳广告接触度', '麦当劳就职意愿', '麦当劳股票',\n",
       "       '麦当劳综合评价', '必胜客企业认知度', '必胜客广告接触度', '必胜客就职意愿', '必胜客股票', '必胜客综合评价'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "df3.columns[df3.dtypes=='category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>float64</td>\n",
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      "text/plain": [
       "                 0\n",
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       "价格昂贵       float64\n",
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       "情感维度       float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df2.astype({'问卷编号':'Int64'})\n",
    "df4.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                 0\n",
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       "职业          object\n",
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       "情感维度       float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df3.astype({'年龄':'Int64'})\n",
    "df3.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>伙食费</th>\n",
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       "      <th>快乐童年</th>\n",
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       "      <th>洋溢青春</th>\n",
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       "      <th>其他感受</th>\n",
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       "      <th>德克士类型感知</th>\n",
       "      <td>category</td>\n",
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       "      <th>服务态度</th>\n",
       "      <td>category</td>\n",
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       "      <th>健康安全</th>\n",
       "      <td>float64</td>\n",
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       "      <th>快速便捷</th>\n",
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       "      <th>价格昂贵</th>\n",
       "      <td>float64</td>\n",
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       "      <th>口感不好</th>\n",
       "      <td>float64</td>\n",
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       "      <th>品类太少</th>\n",
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       "      <th>其他评价</th>\n",
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       "      <th>快速好吃</th>\n",
       "      <td>float64</td>\n",
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       "      <th>服务好</th>\n",
       "      <td>float64</td>\n",
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       "      <th>种类多</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价格便宜</th>\n",
       "      <td>float64</td>\n",
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       "      <th>其他吸引因素</th>\n",
       "      <td>float64</td>\n",
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       "      <td>float64</td>\n",
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       "      <th>送餐速度</th>\n",
       "      <td>float64</td>\n",
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       "    <tr>\n",
       "      <th>就餐环境</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品价格</th>\n",
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       "      <th>促销活动</th>\n",
       "      <td>float64</td>\n",
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       "      <th>产品种类</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
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       "      <th>其他改进因素</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
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       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士股票</th>\n",
       "      <td>category</td>\n",
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       "      <th>德克士综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
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       "      <th>肯德基企业认知度</th>\n",
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       "      <td>category</td>\n",
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       "      <th>肯德基综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>认知维度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>情感维度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "问卷编号       float64\n",
       "调查员         object\n",
       "性别        category\n",
       "年龄           Int64\n",
       "职业          object\n",
       "伙食费       category\n",
       "常去的快餐店    category\n",
       "欢乐聚餐       float64\n",
       "快乐童年       float64\n",
       "都市生活       float64\n",
       "洋溢青春       float64\n",
       "其他感受       float64\n",
       "德克士类型感知   category\n",
       "服务态度      category\n",
       "健康安全       float64\n",
       "快速便捷       float64\n",
       "价格昂贵       float64\n",
       "口感不好       float64\n",
       "品类太少       float64\n",
       "其他评价       float64\n",
       "快速好吃       float64\n",
       "服务好        float64\n",
       "种类多        float64\n",
       "价格便宜       float64\n",
       "其他吸引因素     float64\n",
       "服务态度需改进    float64\n",
       "送餐速度       float64\n",
       "就餐环境       float64\n",
       "产品价格         Int64\n",
       "促销活动       float64\n",
       "产品种类       float64\n",
       "其他改进因素     float64\n",
       "德克士企业认知度  category\n",
       "德克士广告接触度  category\n",
       "德克士就职意愿   category\n",
       "德克士股票     category\n",
       "德克士综合评价   category\n",
       "肯德基企业认知度  category\n",
       "肯德基广告接触度  category\n",
       "肯德基就职意愿   category\n",
       "肯德基股票     category\n",
       "肯德基综合评价   category\n",
       "麦当劳企业认知度  category\n",
       "麦当劳广告接触度  category\n",
       "麦当劳就职意愿   category\n",
       "麦当劳股票     category\n",
       "麦当劳综合评价   category\n",
       "必胜客企业认知度  category\n",
       "必胜客广告接触度  category\n",
       "必胜客就职意愿   category\n",
       "必胜客股票     category\n",
       "必胜客综合评价   category\n",
       "认知维度       float64\n",
       "情感维度       float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df4.astype({'产品价格':'Int64'})\n",
    "df4.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "问卷编号       float64\n",
       "调查员         object\n",
       "性别        category\n",
       "年龄           Int64\n",
       "职业          object\n",
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       "必胜客股票     category\n",
       "必胜客综合评价   category\n",
       "认知维度       float64\n",
       "情感维度       float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df4.astype({'产品种类':'Int64'})\n",
    "df5.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "一般    201\n",
       "不错    130\n",
       "很好     29\n",
       "不好     15\n",
       "Name: 服务态度, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5['服务态度'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男性    188\n",
      "女性    187\n",
      "Name: 性别, dtype: int64\n",
      "六百到九百     125\n",
      "九百到一千二    101\n",
      "一千二以上      79\n",
      "三百到六百      70\n",
      "Name: 伙食费, dtype: int64\n",
      "肯德基       114\n",
      "德克士        75\n",
      "从不去快餐店     74\n",
      "麦当劳        54\n",
      "必胜客        39\n",
      "其他         19\n",
      "Name: 常去的快餐店, dtype: int64\n",
      "普遍大众    172\n",
      "时尚新颖     82\n",
      "中西合璧     77\n",
      "古板单调     28\n",
      "其他类型     16\n",
      "Name: 德克士类型感知, dtype: int64\n",
      "一般    201\n",
      "不错    130\n",
      "很好     29\n",
      "不好     15\n",
      "Name: 服务态度, dtype: int64\n",
      "一点印象都没有    115\n",
      "只知道名字      114\n",
      "只知道几项       84\n",
      "大致了解        56\n",
      "非常了解         6\n",
      "Name: 德克士企业认知度, dtype: int64\n",
      "偶尔看到     144\n",
      "从未看过     101\n",
      "有时会看到     84\n",
      "常常会看到     46\n",
      "Name: 德克士广告接触度, dtype: int64\n",
      "不想去            147\n",
      "到该公司也不错        107\n",
      "不知道             83\n",
      "一定想办法到该公司工作     38\n",
      "Name: 德克士就职意愿, dtype: int64\n",
      "不想买      142\n",
      "不知道      101\n",
      "买了也不错     85\n",
      "一定会买      47\n",
      "Name: 德克士股票, dtype: int64\n",
      "二流     148\n",
      "一流      79\n",
      "不知道     75\n",
      "三流      73\n",
      "Name: 德克士综合评价, dtype: int64\n",
      "只知道名字      127\n",
      "只知道几项      122\n",
      "大致了解        80\n",
      "一点印象都没有     31\n",
      "非常了解        15\n",
      "Name: 肯德基企业认知度, dtype: int64\n",
      "偶尔看到     113\n",
      "常常会看到    113\n",
      "有时会看到    106\n",
      "从未看过      43\n",
      "Name: 肯德基广告接触度, dtype: int64\n",
      "到该公司也不错        129\n",
      "不想去            128\n",
      "不知道             81\n",
      "一定想办法到该公司工作     37\n",
      "Name: 肯德基就职意愿, dtype: int64\n",
      "买了也不错    122\n",
      "不想买      109\n",
      "不知道       94\n",
      "一定会买      50\n",
      "Name: 肯德基股票, dtype: int64\n",
      "二流     178\n",
      "一流     118\n",
      "不知道     49\n",
      "三流      30\n",
      "Name: 肯德基综合评价, dtype: int64\n",
      "只知道几项      140\n",
      "只知道名字      126\n",
      "大致了解        69\n",
      "一点印象都没有     35\n",
      "非常了解         5\n",
      "Name: 麦当劳企业认知度, dtype: int64\n",
      "有时会看到    121\n",
      "偶尔看到     116\n",
      "常常会看到     97\n",
      "从未看过      41\n",
      "Name: 麦当劳广告接触度, dtype: int64\n",
      "不想去            134\n",
      "到该公司也不错        130\n",
      "不知道             79\n",
      "一定想办法到该公司工作     32\n",
      "Name: 麦当劳就职意愿, dtype: int64\n",
      "不想买      125\n",
      "不知道      112\n",
      "买了也不错    104\n",
      "一定会买      34\n",
      "Name: 麦当劳股票, dtype: int64\n",
      "二流     153\n",
      "一流     110\n",
      "不知道     75\n",
      "三流      37\n",
      "Name: 麦当劳综合评价, dtype: int64\n",
      "只知道名字      139\n",
      "只知道几项       99\n",
      "一点印象都没有     71\n",
      "大致了解        56\n",
      "非常了解        10\n",
      "Name: 必胜客企业认知度, dtype: int64\n",
      "偶尔看到     120\n",
      "有时会看到    110\n",
      "从未看过      80\n",
      "常常会看到     65\n",
      "Name: 必胜客广告接触度, dtype: int64\n",
      "不想去            148\n",
      "不知道            114\n",
      "到该公司也不错         84\n",
      "一定想办法到该公司工作     29\n",
      "Name: 必胜客就职意愿, dtype: int64\n",
      "不知道      137\n",
      "不想买      120\n",
      "买了也不错     84\n",
      "一定会买      34\n",
      "Name: 必胜客股票, dtype: int64\n",
      "二流     136\n",
      "一流     100\n",
      "不知道     83\n",
      "三流      56\n",
      "Name: 必胜客综合评价, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for col in df5.columns[df5.dtypes=='category']:\n",
    "    print(df5[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男性    188\n",
      "女性    187\n",
      "Name: 性别, dtype: int64\n",
      "六百到九百     125\n",
      "九百到一千二    101\n",
      "一千二以上      79\n",
      "三百到六百      70\n",
      "Name: 伙食费, dtype: int64\n",
      "肯德基       114\n",
      "德克士        75\n",
      "从不去快餐店     74\n",
      "麦当劳        54\n",
      "必胜客        39\n",
      "其他         19\n",
      "Name: 常去的快餐店, dtype: int64\n",
      "普遍大众    172\n",
      "时尚新颖     82\n",
      "中西合璧     77\n",
      "古板单调     28\n",
      "其他类型     16\n",
      "Name: 德克士类型感知, dtype: int64\n",
      "一般    201\n",
      "不错    130\n",
      "很好     29\n",
      "不好     15\n",
      "Name: 服务态度, dtype: int64\n",
      "一点印象都没有    115\n",
      "只知道名字      114\n",
      "只知道几项       84\n",
      "大致了解        56\n",
      "非常了解         6\n",
      "Name: 德克士企业认知度, dtype: int64\n",
      "偶尔看到     144\n",
      "从未看过     101\n",
      "有时会看到     84\n",
      "常常会看到     46\n",
      "Name: 德克士广告接触度, dtype: int64\n",
      "不想去            147\n",
      "到该公司也不错        107\n",
      "不知道             83\n",
      "一定想办法到该公司工作     38\n",
      "Name: 德克士就职意愿, dtype: int64\n",
      "不想买      142\n",
      "不知道      101\n",
      "买了也不错     85\n",
      "一定会买      47\n",
      "Name: 德克士股票, dtype: int64\n",
      "二流     148\n",
      "一流      79\n",
      "不知道     75\n",
      "三流      73\n",
      "Name: 德克士综合评价, dtype: int64\n",
      "只知道名字      127\n",
      "只知道几项      122\n",
      "大致了解        80\n",
      "一点印象都没有     31\n",
      "非常了解        15\n",
      "Name: 肯德基企业认知度, dtype: int64\n",
      "偶尔看到     113\n",
      "常常会看到    113\n",
      "有时会看到    106\n",
      "从未看过      43\n",
      "Name: 肯德基广告接触度, dtype: int64\n",
      "到该公司也不错        129\n",
      "不想去            128\n",
      "不知道             81\n",
      "一定想办法到该公司工作     37\n",
      "Name: 肯德基就职意愿, dtype: int64\n",
      "买了也不错    122\n",
      "不想买      109\n",
      "不知道       94\n",
      "一定会买      50\n",
      "Name: 肯德基股票, dtype: int64\n",
      "二流     178\n",
      "一流     118\n",
      "不知道     49\n",
      "三流      30\n",
      "Name: 肯德基综合评价, dtype: int64\n",
      "只知道几项      140\n",
      "只知道名字      126\n",
      "大致了解        69\n",
      "一点印象都没有     35\n",
      "非常了解         5\n",
      "Name: 麦当劳企业认知度, dtype: int64\n",
      "有时会看到    121\n",
      "偶尔看到     116\n",
      "常常会看到     97\n",
      "从未看过      41\n",
      "Name: 麦当劳广告接触度, dtype: int64\n",
      "不想去            134\n",
      "到该公司也不错        130\n",
      "不知道             79\n",
      "一定想办法到该公司工作     32\n",
      "Name: 麦当劳就职意愿, dtype: int64\n",
      "不想买      125\n",
      "不知道      112\n",
      "买了也不错    104\n",
      "一定会买      34\n",
      "Name: 麦当劳股票, dtype: int64\n",
      "二流     153\n",
      "一流     110\n",
      "不知道     75\n",
      "三流      37\n",
      "Name: 麦当劳综合评价, dtype: int64\n",
      "只知道名字      139\n",
      "只知道几项       99\n",
      "一点印象都没有     71\n",
      "大致了解        56\n",
      "非常了解        10\n",
      "Name: 必胜客企业认知度, dtype: int64\n",
      "偶尔看到     120\n",
      "有时会看到    110\n",
      "从未看过      80\n",
      "常常会看到     65\n",
      "Name: 必胜客广告接触度, dtype: int64\n",
      "不想去            148\n",
      "不知道            114\n",
      "到该公司也不错         84\n",
      "一定想办法到该公司工作     29\n",
      "Name: 必胜客就职意愿, dtype: int64\n",
      "不知道      137\n",
      "不想买      120\n",
      "买了也不错     84\n",
      "一定会买      34\n",
      "Name: 必胜客股票, dtype: int64\n",
      "二流     136\n",
      "一流     100\n",
      "不知道     83\n",
      "三流      56\n",
      "Name: 必胜客综合评价, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "mytools.print_all_cats(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('性别==22')\n",
    "df5.loc[322,'性别'] = '女性'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>女性</td>\n",
       "      <td>188</td>\n",
       "      <td>50.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>男性</td>\n",
       "      <td>187</td>\n",
       "      <td>49.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>总和</td>\n",
       "      <td>375</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   性别   个数    百分比\n",
       "0  女性  188  50.13\n",
       "1  男性  187  49.87\n",
       "2  总和  375  100.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytools.gen_percent_table(df5,'性别')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('服务态度==0.0')\n",
    "df5.loc[304,'服务态度'] = '不错'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('德克士综合评价==0.0')\n",
    "df5.loc[253,'德克士综合评价'] = '一流'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('麦当劳广告接触度==5.0')\n",
    "df5.loc[105,'麦当劳广告接触度'] = '从未看过'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    375.000000\n",
       "mean      24.282667\n",
       "std        6.449316\n",
       "min       10.000000\n",
       "25%       20.000000\n",
       "50%       22.000000\n",
       "75%       26.000000\n",
       "max       60.000000\n",
       "Name: 年龄, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"年龄\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count       375.000000\n",
      "mean        537.909333\n",
      "std        5153.219438\n",
      "min           1.000000\n",
      "25%         142.500000\n",
      "50%         260.000000\n",
      "75%         387.500000\n",
      "max      100000.000000\n",
      "Name: 问卷编号, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.274667\n",
      "std        0.446942\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 欢乐聚餐, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.130667\n",
      "std        0.337486\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 快乐童年, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.298667\n",
      "std        0.458285\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 都市生活, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.152000\n",
      "std        0.359501\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 洋溢青春, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.189333\n",
      "std        0.392297\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他感受, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.144000\n",
      "std        0.351559\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 健康安全, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.568000\n",
      "std        0.496016\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        1.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 快速便捷, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.328000\n",
      "std        0.470112\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 价格昂贵, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.165333\n",
      "std        0.371977\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 口感不好, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.162667\n",
      "std        0.369554\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 品类太少, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.080000\n",
      "std        0.271656\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他评价, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.461333\n",
      "std        0.499169\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 快速好吃, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.186667\n",
      "std        0.390164\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 服务好, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.293333\n",
      "std        0.455898\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 种类多, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.157333\n",
      "std        0.364601\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 价格便宜, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.090667\n",
      "std        0.287518\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他吸引因素, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.320000\n",
      "std        0.467099\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 服务态度需改进, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.306667\n",
      "std        0.461726\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 送餐速度, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.360000\n",
      "std        0.480641\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 就餐环境, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.234667\n",
      "std        0.424356\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 促销活动, dtype: float64\n",
      "count    375.000000\n",
      "mean       0.048000\n",
      "std        0.214052\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他改进因素, dtype: float64\n",
      "count    375.000000\n",
      "mean       6.429333\n",
      "std        3.083455\n",
      "min        0.000000\n",
      "25%        4.000000\n",
      "50%        7.000000\n",
      "75%        9.000000\n",
      "max       12.000000\n",
      "Name: 认知维度, dtype: float64\n",
      "count    375.000000\n",
      "mean       6.989333\n",
      "std        2.586450\n",
      "min        0.000000\n",
      "25%        4.500000\n",
      "50%        7.000000\n",
      "75%        9.000000\n",
      "max       12.000000\n",
      "Name: 情感维度, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "mytools.print_all_int(df5)\n",
    "mytools.print_all_float(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = '肯德基企业认知度 == \"大致了解\" and 肯德基广告接触度 == \"偶尔看到\"'\n",
    "temp = df5.query(c1)[['肯德基企业认知度','肯德基广告接触度']]\n",
    "df6 = df5.drop(temp.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df6.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = df5['常去的快餐店'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>常去的快餐店</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>肯德基</td>\n",
       "      <td>114</td>\n",
       "      <td>30.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>德克士</td>\n",
       "      <td>75</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>从不去快餐店</td>\n",
       "      <td>74</td>\n",
       "      <td>19.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>麦当劳</td>\n",
       "      <td>54</td>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>必胜客</td>\n",
       "      <td>39</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>其他</td>\n",
       "      <td>19</td>\n",
       "      <td>5.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>总和</td>\n",
       "      <td>375</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   常去的快餐店   个数    百分比\n",
       "0     肯德基  114   30.4\n",
       "1     德克士   75   20.0\n",
       "2  从不去快餐店   74  19.73\n",
       "3     麦当劳   54   14.4\n",
       "4     必胜客   39   10.4\n",
       "5      其他   19   5.07\n",
       "6      总和  375  100.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = pd.DataFrame()\n",
    "b['常去的快餐店'] = df5['常去的快餐店'].value_counts().index\n",
    "b['个数'] = df5['常去的快餐店'].value_counts().values\n",
    "b['百分比'] = df5['常去的快餐店'].value_counts(normalize=True).values * 100\n",
    "b['百分比'] = b['百分比'].apply(lambda x: round(x, 2))\n",
    "total_row = pd.Series({'常去的快餐店':'总和','个数':b['个数'].sum(),'百分比':b['百分比'].sum()}).to_frame().T\n",
    "pd.concat([b,total_row],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>常去的快餐店</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>肯德基</td>\n",
       "      <td>114</td>\n",
       "      <td>30.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>德克士</td>\n",
       "      <td>75</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>从不去快餐店</td>\n",
       "      <td>74</td>\n",
       "      <td>19.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>麦当劳</td>\n",
       "      <td>54</td>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>必胜客</td>\n",
       "      <td>39</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>其他</td>\n",
       "      <td>19</td>\n",
       "      <td>5.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>总和</td>\n",
       "      <td>375</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   常去的快餐店   个数    百分比\n",
       "0     肯德基  114   30.4\n",
       "1     德克士   75   20.0\n",
       "2  从不去快餐店   74  19.73\n",
       "3     麦当劳   54   14.4\n",
       "4     必胜客   39   10.4\n",
       "5      其他   19   5.07\n",
       "6      总和  375  100.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytools.gen_percent_table(df5,'常去的快餐店')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.show_bar(df5,'常去的快餐店')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>肯德基综合评价</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "      <th>累计百分比（%）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>一流</td>\n",
       "      <td>118</td>\n",
       "      <td>31.47</td>\n",
       "      <td>31.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>二流</td>\n",
       "      <td>178</td>\n",
       "      <td>47.47</td>\n",
       "      <td>78.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>三流</td>\n",
       "      <td>30</td>\n",
       "      <td>8.0</td>\n",
       "      <td>86.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>不知道</td>\n",
       "      <td>49</td>\n",
       "      <td>13.07</td>\n",
       "      <td>100.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>总和</td>\n",
       "      <td>375</td>\n",
       "      <td>100.01</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  肯德基综合评价   个数     百分比 累计百分比（%）\n",
       "0      一流  118   31.47    31.47\n",
       "1      二流  178   47.47    78.94\n",
       "2      三流   30     8.0    86.94\n",
       "3     不知道   49   13.07   100.01\n",
       "4      总和  375  100.01         "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.api.types import CategoricalDtype\n",
    "cat_dtype = CategoricalDtype(\n",
    "    categories=['一流','二流', '三流','不知道'], ordered=True)\n",
    "df = df.astype({'肯德基综合评价':cat_dtype})\n",
    "mytools.ordinal_desc(df,'肯德基综合评价')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.show_bar(df,'肯德基综合评价',sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    375.000000\n",
       "mean       6.200000\n",
       "std        2.981225\n",
       "min        0.000000\n",
       "25%        4.000000\n",
       "50%        7.000000\n",
       "75%        9.000000\n",
       "max       11.000000\n",
       "Name: 认知维度, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['认知维度']= df['德克士综合评价'].cat.codes + df['肯德基综合评价'].cat.codes + df['麦当劳综合评价'].cat.codes+ df['必胜客综合评价'].cat.codes\n",
    "df['认知维度'].describe()\n",
    "df['认知维度'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='认知维度', ylabel='Count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.histplot(data=df, x=\"认知维度\",binwidth=1,kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.crosstab(\n",
    "        df['伙食费'],\n",
    "        df['调查员'],\n",
    "        normalize='columns',\n",
    "        margins=True,\n",
    "        margins_name='合计',\n",
    "    )*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>调查员</th>\n",
       "      <th>严</th>\n",
       "      <th>代宏娟</th>\n",
       "      <th>任宁</th>\n",
       "      <th>余娇</th>\n",
       "      <th>党仁兰</th>\n",
       "      <th>党思瑶</th>\n",
       "      <th>刘璐</th>\n",
       "      <th>努尔</th>\n",
       "      <th>吴培兰</th>\n",
       "      <th>吴欣浃</th>\n",
       "      <th>...</th>\n",
       "      <th>陈丹丹</th>\n",
       "      <th>陈文琪</th>\n",
       "      <th>陈燕</th>\n",
       "      <th>马浩</th>\n",
       "      <th>魏良英</th>\n",
       "      <th>麻娇</th>\n",
       "      <th>黄冬佳</th>\n",
       "      <th>黄少莲</th>\n",
       "      <th>黄智强</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>伙食费</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一千二以上</th>\n",
       "      <td>0.00</td>\n",
       "      <td>22.22</td>\n",
       "      <td>27.27</td>\n",
       "      <td>38.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>66.67</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.57</td>\n",
       "      <td>18.18</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.08</td>\n",
       "      <td>46.67</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.29</td>\n",
       "      <td>21.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>三百到六百</th>\n",
       "      <td>28.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.69</td>\n",
       "      <td>33.33</td>\n",
       "      <td>11.11</td>\n",
       "      <td>33.33</td>\n",
       "      <td>57.14</td>\n",
       "      <td>14.29</td>\n",
       "      <td>18.18</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22.22</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>44.44</td>\n",
       "      <td>14.29</td>\n",
       "      <td>18.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>九百到一千二</th>\n",
       "      <td>14.29</td>\n",
       "      <td>22.22</td>\n",
       "      <td>36.36</td>\n",
       "      <td>30.77</td>\n",
       "      <td>33.33</td>\n",
       "      <td>11.11</td>\n",
       "      <td>22.22</td>\n",
       "      <td>14.29</td>\n",
       "      <td>42.86</td>\n",
       "      <td>36.36</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>38.46</td>\n",
       "      <td>13.33</td>\n",
       "      <td>11.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.22</td>\n",
       "      <td>42.86</td>\n",
       "      <td>26.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>六百到九百</th>\n",
       "      <td>57.14</td>\n",
       "      <td>55.56</td>\n",
       "      <td>36.36</td>\n",
       "      <td>23.08</td>\n",
       "      <td>33.33</td>\n",
       "      <td>11.11</td>\n",
       "      <td>11.11</td>\n",
       "      <td>28.57</td>\n",
       "      <td>14.29</td>\n",
       "      <td>27.27</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.08</td>\n",
       "      <td>40.00</td>\n",
       "      <td>33.33</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.33</td>\n",
       "      <td>28.57</td>\n",
       "      <td>33.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "调查员         严    代宏娟     任宁     余娇    党仁兰    党思瑶     刘璐     努尔    吴培兰    吴欣浃  \\\n",
       "伙食费                                                                            \n",
       "一千二以上    0.00  22.22  27.27  38.46   0.00  66.67  33.33   0.00  28.57  18.18   \n",
       "三百到六百   28.57   0.00   0.00   7.69  33.33  11.11  33.33  57.14  14.29  18.18   \n",
       "九百到一千二  14.29  22.22  36.36  30.77  33.33  11.11  22.22  14.29  42.86  36.36   \n",
       "六百到九百   57.14  55.56  36.36  23.08  33.33  11.11  11.11  28.57  14.29  27.27   \n",
       "\n",
       "调查员     ...   陈丹丹    陈文琪     陈燕     马浩   魏良英    麻娇   黄冬佳    黄少莲    黄智强     合计  \n",
       "伙食费     ...                                                                    \n",
       "一千二以上   ...   0.0  23.08  46.67  33.33   0.0  20.0  25.0   0.00  14.29  21.07  \n",
       "三百到六百   ...  20.0  15.38   0.00  22.22  40.0  30.0  50.0  44.44  14.29  18.67  \n",
       "九百到一千二  ...  50.0  38.46  13.33  11.11   0.0  10.0  25.0  22.22  42.86  26.93  \n",
       "六百到九百   ...  30.0  23.08  40.00  33.33  60.0  40.0   0.0  33.33  28.57  33.33  \n",
       "\n",
       "[4 rows x 39 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tau_y值为：0.141，该值属于极弱相关或不相关。'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau_y = mytools.goodmanKruska_tau_y(df, '调查员', '伙食费')\n",
    "f'tau_y值为：{tau_y:.3f}，该值属于{mytools.draw_on_corr(tau_y)}。'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>职业</th>\n",
       "      <th></th>\n",
       "      <th>个体</th>\n",
       "      <th>个体户</th>\n",
       "      <th>促销员</th>\n",
       "      <th>保安</th>\n",
       "      <th>公关经理</th>\n",
       "      <th>公务员</th>\n",
       "      <th>公司职员</th>\n",
       "      <th>农民工</th>\n",
       "      <th>医生</th>\n",
       "      <th>...</th>\n",
       "      <th>警察</th>\n",
       "      <th>退休</th>\n",
       "      <th>送饮员</th>\n",
       "      <th>部门主管</th>\n",
       "      <th>银行</th>\n",
       "      <th>银行白领</th>\n",
       "      <th>银行职员</th>\n",
       "      <th>销售</th>\n",
       "      <th>餐饮</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>伙食费</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>六百到九百</th>\n",
       "      <td>42.86</td>\n",
       "      <td>35.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>九百到一千二</th>\n",
       "      <td>42.86</td>\n",
       "      <td>28.57</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>三百到六百</th>\n",
       "      <td>0.00</td>\n",
       "      <td>7.14</td>\n",
       "      <td>16.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>18.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一千二以上</th>\n",
       "      <td>14.29</td>\n",
       "      <td>28.57</td>\n",
       "      <td>50.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.67</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "职业                个体    个体户    促销员     保安   公关经理    公务员   公司职员    农民工     医生  \\\n",
       "伙食费                                                                            \n",
       "六百到九百   42.86  35.71   0.00    0.0  33.33    0.0    0.0   0.00  100.0    0.0   \n",
       "九百到一千二  42.86  28.57  33.33    0.0   0.00    0.0    0.0  66.67    0.0  100.0   \n",
       "三百到六百    0.00   7.14  16.67    0.0   0.00    0.0  100.0   0.00    0.0    0.0   \n",
       "一千二以上   14.29  28.57  50.00  100.0  66.67  100.0    0.0  33.33    0.0    0.0   \n",
       "\n",
       "职业      ...     警察     退休    送饮员   部门主管     银行   银行白领   银行职员    销售     餐饮  \\\n",
       "伙食费     ...                                                                 \n",
       "六百到九百   ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   0.0    0.0   \n",
       "九百到一千二  ...  100.0  100.0    0.0    0.0    0.0    0.0  100.0  25.0    0.0   \n",
       "三百到六百   ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   0.0  100.0   \n",
       "一千二以上   ...    0.0    0.0  100.0  100.0  100.0  100.0    0.0  75.0    0.0   \n",
       "\n",
       "职业         合计  \n",
       "伙食费            \n",
       "六百到九百   33.33  \n",
       "九百到一千二  26.93  \n",
       "三百到六百   18.67  \n",
       "一千二以上   21.07  \n",
       "\n",
       "[4 rows x 61 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_dtype = CategoricalDtype(\n",
    "    categories=['六百到九百','九百到一千二', '三百到六百','一千二以上'], ordered=True)\n",
    "df = df.astype({'伙食费':cat_dtype})\n",
    "\n",
    "result = pd.crosstab(\n",
    "        df['伙食费'],\n",
    "        df['职业'],\n",
    "        normalize='columns',\n",
    "        margins=True,\n",
    "        margins_name='合计',\n",
    "    )*100\n",
    "result.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['情感维度']= df['德克士就职意愿'].cat.codes + df['肯德基就职意愿'].cat.codes + df['麦当劳就职意愿'].cat.codes+ df['必胜客就职意愿'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.gen_scatter(df,'情感维度','认知维度')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='常去的快餐店', ylabel='认知维度'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(\n",
    "        x='常去的快餐店',\n",
    "        y='认知维度',\n",
    "        data=df,\n",
    "        color='white',\n",
    "        linewidth=1,\n",
    "        width=0.5,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'相关比率为：0.038，按照J.Cohen 提出的标准(0.01时为小效应，0.06时为中等效应，而0.14为大效应)，强度为低度相关。'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.formula.api import ols\n",
    "model = ols('认知维度 ~ 常去的快餐店', df).fit()\n",
    "eta_2 = model.rsquared\n",
    "f\"\"\"相关比率为：{eta_2:.3f}，按照J.Cohen 提出的标准(0.01时为小效应，0.06时为中等效应，而0.14为大效应)，强度为{mytools.draw_on_eta2(eta_2)}。\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = mytools.set_label_to_code(df, metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "mytools.to_sav(df_clean,metadata,'./data/企业形象调查原始数据.sav')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e5b51f9075b4cc1ea8d9810577a26807122690438b3a6e6e05129a402faed2ba"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
